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importantly, they can lead to incorrect analysis and
wrong conclusions. To avoid losing valuable data, it
is critical to develop robust methods for cleaning out
EEG recordings from OA’s. For the purpose of
evaluating the state of the art in the detection and
elimination/reduction of OA’s, we implemented 12
promising methods found in the literature. We
evaluated the performance of all the methods in
terms of their ability to correctly detect OA zones in
EEG recordings, as compared to a ground truth
established visually. Results suggest that methods
based on adaptive filtering such as LMS and RLS, as
well as their combination with the SWT are the best
methods to successfully detect OA zones in EEG
recordings. These methods have higher values of
sensitivity and specificity, and better ROC curves,
than the other correction methods.
ACKNOWLEDGEMENTS
The authors thank IFSTTAR for making available
one of their driving simulator software, and the
“Centre d’Etudes des Troubles de l’Eveil et du
Sommeil” (CETES) for making available their
facilities and equipments.
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